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Mixolar 4x7b by shadowml

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Model Card on HF 🤗: https://huggingface.co/shadowml/Mixolar-4x7b 

Mixolar 4x7b Benchmarks

MMLU Pro: 25.62
GPQA: 5.7
MUSR: 12.72
BBH: 32.73
IFEval: 38.93 vs 88 (so35)-55.8%
ARC: 71.08 vs 96.7 (so35)-26.5%
HellaSwag: 88.44 vs 95.3 (gpt4)-7.2%
MMLU: 66.29 vs 88.3 (so35)-24.9%
TruthfulQA: 71.81 vs 59 (gpt4)21.7%
WinoGrande: 83.58 vs 87.5 (gpt4)-4.5%
GSM8K: 63.91 vs 96.4 (so35)-33.7%
MATH Lvl 5: 5.82
LLME Score: 0.23048
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Mixolar 4x7b (shadowml/Mixolar-4x7b)
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Mixolar 4x7b Parameters and Internals

Model Type 
text-generation
LLM NameMixolar 4x7b
Repository 🤗https://huggingface.co/shadowml/Mixolar-4x7b 
Model Size36.1b
Required VRAM72.3 GB
Updated2025-09-23
Maintainershadowml
Model Typemixtral
Instruction-BasedYes
Model Files  9.9 GB: 1-of-8   10.0 GB: 2-of-8   10.0 GB: 3-of-8   10.0 GB: 4-of-8   10.0 GB: 5-of-8   10.0 GB: 6-of-8   10.0 GB: 7-of-8   2.4 GB: 8-of-8
Model ArchitectureMixtralForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.36.2
Tokenizer ClassLlamaTokenizer
Padding Token<s>
Vocabulary Size32000
Torch Data Typefloat16

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Rank the Mixolar 4x7b Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124
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